Dataset Information

Predicting implicit attitudes with natural language data.

ABSTRACT: Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures variance in implicit attitudes that is largely unexplained by explicit attitudes. Overall, our results shed light on how implicit attitudes can be measured by combining standard psychological data with large-scale language data. In doing so, we pave the way for highly accurate computational modeling of what people think and feel about the world around them.

SUBMITTER: Bhatia S

PROVIDER: S-EPMC10288542 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Predicting implicit attitudes with natural language data.

Bhatia Sudeep S Walasek Lukasz L

Proceedings of the National Academy of Sciences of the United States of America 20230612 25

Large-scale language datasets and advances in natural language processing offer opportunities for studying people's cognitions and behaviors. We show how representations derived from language can be combined with laboratory-based word norms to predict implicit attitudes for diverse concepts. Our approach achieves substantially higher correlations than existing methods. We also show that our approach is more predictive of implicit attitudes than are explicit attitudes, and that it captures varian ...[more]

PMID: 37307492

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Dataset Information

Predicting implicit attitudes with natural language data.

Publications

Predicting implicit attitudes with natural language data.

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